System and method to locate soft tissue for preoperative planning

ABSTRACT

Methods and systems for determining a patient specific soft tissue location within a joint are discussed. For example, a method can include imaging a target location for an orthopedic implant to collect image data regarding a morphology of the patient, the morphology including at least one of bone size and bone feature. The method can additionally include accessing stored soft tissue data and bone data corresponding to the target location of the orthopedic implant, locating a soft tissue of the patient based at least in part upon the soft tissue data and bone data and the image data, and displaying data including the location of the soft tissue of the patient. This data can be used to created patient specific surgical jigs according to one example of the present application.

CLAIM OF PRIORITY

This application is a continuation of U.S. patent application Ser. No.15/214,071, filed on Jul. 19, 2016, which claims the benefit of U.S.Provisional Patent Application Ser. No. 62/196,496, filed on Jun. 24,2015, the benefit of priority of each of which is claimed hereby, andeach of which is incorporated by reference herein in its entirety.

BACKGROUND

In arthroplasty and some sports medicine surgeries, a damaged joint,such as a knee joint, is replaced with prosthetic implants. Prior toimplantation of the implant, the damaged region of the joint istypically prepared by resecting or otherwise treating regions of thebones to provide surfaces that can align with and therefore accommodatethe implant.

One of the predictors of an orthopedic arthroplasty outcome isappropriate selection and positioning of the prosthetic components.During orthopedic procedures, various tools and instruments are used toassist with prosthetic component selection and placement, including theuse of templates as well as provisional or trial implant prosthetics.Such conventional tools and instruments may lack precision as they mayrely on the user's judgment to assess proper positioning of the devices.In addition, each patient's anatomy being different, proper componentsizing may be required for optimizing the outcome of the surgery. Still,conventional components may only allow patient customization to acertain degree.

Overview

Example systems and methods for determining a patient specific softtissue location within a joint of a patient are described. Based atleast in part on the patient-specific soft tissue location, the examplesystems and methods can also be utilized in preoperative planning, toaid in selection or create a surgical jig and/or to aid in selection ofa prosthesis. According to some examples, the systems and methods can beused in preoperative planning to provide the user with instructions,visual aids, information, recommendations, automated measurements, andso on as to the location, size, and other properties of soft tissue,bone, and/or prostheses relevant to the procedure. In the followingdescription, for purposes of explanation, numerous specific details areset forth in order to provide a thorough understanding of examplesprovided. It will be evident, however, to one skilled in the art thatexamples of the present invention may be practiced without thesespecific details or details may be modified to a degree. It will also beevident that the systems and methods discussed are not limited to theexamples provided and may include other scenarios not specificallydiscussed. For example, the methodologies discussed herein with respectto a knee arthroscopy can be similarly applied to other procedures(e.g., reconstructing the PCL or other ligamentous structures for sportsmedicine surgery, a total hip or total shoulder replacement, amongothers).

Understanding the location of soft tissue structures is important inmany orthopedic procedures. For example, soft tissue location can affectthe size and shape of prosthetic implant installed, as well as the shapeof a jig used to ensure accurate position and orientation of finishinginstruments during bone resection of a knee arthroscopy. For example,understanding the location and shape of the anterior cruciate ligament(ACL) and/or posterior cruciate ligament (PCL) can be beneficial whenplacing both medial and lateral femoral and tibia implants in aunicompartmental knee arthroscopy, making a tibial cut and placing thetibial component in a cruciate retaining total knee arthroscopy, andmaking bone cuts and placing implants in a bi-cruciate sparing totalknee arthroscopy.

Preoperative planning including templating can be performed in clinicalpractice to determine the size and shape of implants and jigs likely tobest fit the anatomy of the individual. In order to further refinepreoperative practice, the current inventors recognized thatunderstanding the location of a patient's soft tissue can beinstructive. Current planning technology does not seek suchunderstanding as it can be expensive to gather, inaccurate, and timeconsuming. Accordingly, the inventors preformed cadaver and imagingstudies on hundreds of knee joints to determine the size and location ofsoft tissue (e.g., ACL, PCL) relative to the morphology (e.g., sizeand/or features) of the tibia and femur. From such measurements, theinventors derived various databases, methodologies, and systems asdisclosed herein. According to some examples provided herein, thedisclosed databases, methodologies, and systems can be used to locatesoft tissue for preoperative planning when used in combination withradiographic or similar medical images of a patient's joint.

To further illustrate the components and methods disclosed herein, anon-limiting list of examples is provided here:

In Example 1, a method can comprise imaging a target location for anorthopedic implant to collect image data regarding a morphology of thepatient, the morphology including at least one of bone size and bonefeature, accessing stored soft tissue data and bone data correspondingto the target location of the orthopedic implant, determining thelocation of a soft tissue of the patient based at least in part upon thesoft tissue data and bone data and the image data, and displaying dataincluding the location of the soft tissue of the patient.

In Example 2, the method of Example 1, can comprise constructing, basedat least in part on the location of the soft tissue, a virtual model ofthe target location, wherein the virtual model displays a contour of thesoft tissue.

In Example 3, the method of any one or any combination of Examples 1 or2, can comprise recommending a prosthesis to best fit one or more of afemur and tibia of the patient.

In Example 4, the method of any one or any combination of Examples 1 to3, can comprise fabricating a patient-specific jig for preparing anarticular surface of a bone in the target location a design of the jigbased at least in part upon the location of the soft tissue.

In Example 5, the method of any one or any combination of Examples 1 to4, wherein the morphology can further include one of a soft tissue shapeand soft tissue location.

In Example 6, the method of any one or any combination of Examples 1 to5, wherein the soft tissue can comprise at least one of an ACL and a PCLand locating the soft tissue can comprise: creating an average of one ormore of an ACL and PCL contour for one or more of an average femur andtibia from the soft tissue data and bone data corresponding to thetarget location of the orthopedic implant, altering one or more of theaverage femur and tibia to match one or more of a femur and tibia of thepatient, and altering one or more of an ACL and PCL contour of thepatient with the step of altering one or more of the average femur andtibia.

In Example 7, the method of any one or any combination of Examples 1 to6, wherein imaging can comprise use of one or more of X-Ray,Fluoroscopy, Computerized Tomography, True size imaging, and MRI.

In Example 8, the method of any one or any combination of Examples 1 to7, can comprise producing one or more of anatomical measurement,instruction, recommendation, information, and visual aid.

In Example 9, a system can comprise a computer including at least oneprocessor and a memory device, the memory device including instructionsthat, when executed by the at least one processor, cause the computerto: access image data of a target location for an orthopedic implant,the image data including data regarding at least one of bone size andbone feature of the patient, access stored soft tissue data and bonedata corresponding to the target location of the orthopedic implant,compare the image data to the soft tissue and bone data, and determine,based at least in part on the soft tissue data and bone data, a locationof the soft tissue within the target location.

In Example 10, the system of Example 9, can further compriseinstructions that cause the computer to construct a virtual model of thetarget location, wherein the virtual model includes a display of acontour of the soft tissue.

In Example 11, the system of any one or any combination of Examples 9and 10, can further comprise instructions that cause the computer torecommend, based at least in part on the location of the soft tissue, aprosthesis to best fit one or more of a femur and tibia of the patient.

In Example 12, the system of any one or any combination of Examples 9 to11, can further comprise instructions that cause the computer to provideinstruction, based at least in part upon the location of the softtissue, regarding a design of a patient-specific jig for preparing anarticular surface of a bone in the target location.

In Example 13, the system of any one or any combination of Examples 9 to12, wherein the image data can include soft tissue shape and soft tissuelocation.

In Example 14, the system of any one of Examples 9 to 13, wherein thesoft tissue can comprise at least one of an ACL and a PCL andinstructions that can cause the computer to determine the location ofthe soft tissue include instructions to cause the computer to: create anaverage of one or more of an ACL and PCL contour for one or more of anaverage femur and tibia from the soft tissue data and bone datacorresponding to the target location of the orthopedic implant, alterone or more of the average femur and tibia to match one or more of afemur and tibia of the patient, and alter one or more of an ACL and PCLcontour of the patient with the step of altering one or more of theaverage femur and tibia.

In Example 15, the system of any one of Examples 9 to 14, can furthercomprise instructions that cause the computer to perform one or more ofproviding at least one anatomical measurement, at least one instruction,at least one recommendation, provide at least one of information, and atleast one visual aid.

In Example 16, a machine-readable storage device can includeinstructions that, when executed by a machine, cause the machine to:access image data of a target location for an orthopedic implant, theimage data including data regarding at least one of bone size and bonefeature of the patient, access stored soft tissue data and bone datacorresponding to the target location of the orthopedic implant, comparethe image data to the soft tissue and bone data, and determine, based atleast in part on the soft tissue data and bone data, a location of thesoft tissue within the target location.

In Example 17, the machine-readable storage device of Example 16, canfurther include instructions to cause the machine to construct a virtualmodel of the target location, wherein the virtual model includes adisplay of a contour of the soft tissue.

In Example 18, the machine-readable storage device of any one or anycombination of Examples 16 and 17, can further include instructions tocause the machine to recommend, based at least in part on the locationof the soft tissue, a prosthesis to best fit one or more of a femur andtibia of the patient.

In Example 19, the machine-readable storage device of any one or anycombination of Examples 16 to 18, can further include instructions tocause the machine to provide instruction, based at least in part uponthe location of the soft tissue, regarding a design of apatient-specific jig for preparing an articular surface of a bone in thetarget location.

In Example 20, the machine-readable storage device of any one or anycombination of Examples 16 to 19, wherein the soft tissue can compriseat least one of an ACL and a PCL and instructions can cause the machineto determine the location of the soft tissue include instructions tocause the machine to: create an average of one or more of an ACL and PCLcontour for one or more of an average femur and tibia from the softtissue data and bone data corresponding to the target location of theorthopedic implant, alter one or more of the average femur and tibia tomatch one or more of a femur and tibia of the patient, and alter one ormore of an ACL and PCL contour of the patient with the step of alteringone or more of the average femur and tibia.

In Example 21, the system or method of any one or any combination ofExamples 1-20 can optionally be configured such that all elements oroptions recited are available to use or select from.

These and other examples and features of the present systems and methodswill be set forth in part in the following Detailed Description. ThisOverview is intended to provide non-limiting examples of the presentsubject matter—it is not intended to provide an exclusive or exhaustiveexplanation. The Detailed Description below is included to providefurther information about the present systems and methods.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. The drawings illustrate generally, by way of example, butnot by way of limitation, various examples discussed in the presentdocument.

FIG. 1 is a block diagram illustrating a system for preoperativeplanning based on location of soft tissue, according to an example ofthe present disclosure.

FIG. 2 is an anterior view of a natural femur and tibia.

FIG. 2A is an elevated view of the tibia of FIG. 2.

FIG. 3 is a perspective view of a tool positioned adjacent a distal endof the femur and adapted to measure the location of the soft tissues,according to an example of the present disclosure.

FIG. 4 is an image of the proximal end of a tibia overlaid with data ascollected by the tool of FIG. 4, according to an example of the presentdisclosure.

FIG. 5 is a flowchart illustrating a method for determining a patientspecific soft tissue location, according to an example of the presentdisclosure.

FIG. 6 is a flowchart illustrating a method for among other thingsselecting a prosthesis and/or jig based on the soft tissue location andother morphology, according to an example of the present disclosure.

FIG. 7 is a virtual representation of the location of the soft tissuesof a patient relative to the tibia, femur, and a prosthesis, accordingto an example of the present disclosure.

FIG. 8 is a view of the soft tissues, the femur and a surgical jig thatcan be created using information on the geometry of the patient's kneeaccording to an example of the present disclosure.

FIG. 9 is a screen shot image from a preoperative planning tool having avisual display with functionality according to systems and methodsdisclosed herein, according to an example of the present disclosure.

DETAILED DESCRIPTION

Example systems and methods for determining a patient specific softtissue location within a joint of a patient are described. Based atleast in part on the patient specific soft tissue locations, the examplesystems and methods can also be utilized in preoperative planning, toaid in selection or creation of a surgical jig and/or to aid inselection of a prosthesis based on the patient specific soft tissuelocations. In the example of FIG. 1, a physician or other personnel canuse a system, such as system 100, to locate soft tissue in theapplicable joint of a patient. The system 100 can also be utilized bythe user to aid in selection of a prosthesis and/or aid in creation of ajig. Such selection or creation can be based upon the location and othercharacteristics of the soft tissue, for example. In FIG. 1, the system100 can include a location system 110 receiving data from one or more ofa soft tissue database 150, a medical imaging system 160, or one or moreadditional databases 170. In some examples, the location system 110 caninclude a user-interface module 115, an image retrieval module 120, aselection module 125, an anatomical geometries module 130, and an imageprocessing engine 140.

In an example, the user-interface module 115 can receive input from auser and provide feedback on the resulting measurements, soft tissuelocations, calculations, and resection locations, for example. Accordingto some examples, the user-interface module 115 can provide guidance forjig and/or prosthesis selection in view of the soft tissue size,location, bone morphology, etc. In one example, the user-interfacemodule 115 can process inputs such as the selection of joint morphology(e.g., bone size, bone features, soft tissue location) on a medicalimage of a region of interest within the medial image of the joint.Additionally, the user-interface module 115 can process inputs andprovide output associated with other aspects of the location system 110.According to some examples, the user-interface module 115 can interfacewith user-interface components, such as a display and user-inputmechanism (e.g., mouse, keyboard, or touch screen).

In an example, the image retrieval module 120 can retrieve a medicalimage for processing by the location system 110 from sources, such asthe soft tissue database 150 or the medical imaging system 160, amongothers. The image retrieval module 120 can communicate directly with themedical imaging system 160 to receive a radiographic (or similar)medical image of a patient's joint for processing by the location system110. Medical image processed by the location system 110 can be of anytype of medical image that depicts internal structures of a patient'sjoint and soft tissue. Technologies such as X-Ray, Fluoroscopy,Computerized Tomography (CT), True size imaging (EOS™), and MRI can allproduce usable images. Other imaging technologies can be used with themethods and systems discussed herein.

The image processing engine 140 can run various imagine processingalgorithms on the medical images retrieved by the image retrieval module120. The image processing engine 140 can use image processing algorithmssuch as thresholding, edge detection, contrast detection, contrast-edgedetection, and other known image processing techniques to perform theautomated measurements discussed in more detail below.

According to the example of FIG. 1, the anatomical geometries module 130can use data generated by the image processing engine 140 and/or thelocation system 110 to perform calculations to describe or characterizethe geometry of one or more bones of the joint. These calculations candetermine, for example, anterior/posterior and/or medial/lateral bonedimensions, bone axes/landmarks/positions, relative positions betweenbones, curvature and surface topography of the bone or articularsurface, and/or soft tissue attachment size and/or location, and thelike. Bone landmarks can include the size, shape, position of the medialand lateral condyle, medial and lateral epicondyle, tibial tuberosity,trochlear groove, intercondylar notch, PCL facet, tibial eminence,and/or tibular head, for example. According to some examples, theselection module 125 can use the calculations generated by theanatomical geometries module 130 and the location system 110 to selectan appropriate patient specific prosthetic implant and/or to prototypeor select a patient specific jig. For example, the prosthesis selectionmodule 125 can utilize the soft tissue location data as supplied by thelocation system 110 and the dimensions of the available prostheticimplants to optimize fit. Such calculation can be based on the locationof the soft tissue relative to the measured geometries of the patient'sdistal femur and/or proximal tibia in the case of a knee arthroplasty,for example. According to some examples, the prosthesis selection module125 can look up prosthetic implant sizing information from the database170.

According to one example, a method is disclosed that utilizes imagingdata from a patient and performs calculations from the imaging dataincluding determining locations of relevant soft tissue structures. Fromthe calculations, surgical decisions including the size of implants andthe placement of the implant on the patient can be determined. Thesurgical decisions can be visualized electronically prior to beingimplemented. Based upon the visualization, the surgeon can alter his orher decision as desired.

FIG. 2 illustrates a natural femur 210 and tibia 212. The femur 210 caninclude medial 214 and lateral 216 condyles at a distal end of the femur210. Various soft tissues (e.g., ligaments) can be attached to the femur210 and/or the tibia 212. For example, the anterior cruciate ligament(ACL) 218 can extend from an anterior side of the tibia 212 to the femur210, and the posterior cruciate ligament (PCL) 220 can extend from aposterior side of the tibia 212 to the femur 210. FIG. 2A is a top viewof the tibia 212 and further illustrates some of these soft tissues aswell as a medial meniscus 222 and a lateral meniscus 224 that arelocated between the tibia 212 and the medial 214 and lateral 216condyles.

FIG. 3 illustrates a sensor 310 that can be adapted to sense themorphology (e.g., bone size, bone features, soft tissue shape andlocation) in a patient's joint 312. The sensor 310 can be part of aportable coordinate measurement machine (CMM). It is used to measure thethree dimensional location of a point with respect to the base of themachine. It can be used to map the surface of a bone and also thelocation of soft tissue structures on that bone. The informationgathered by the sensor 310 can be used in the soft tissue database 150and/or databases 170 as described in FIG. 1. In the example of FIG. 3,the sensor 310 is illustrated measuring a size and other geometry of adistal femur 314. According to some examples, the sensor 310 can be usedto measure a location of where the ACL and PCL connect to the distalfemur 314. The size and geometry of features of the distal femur (e.g.,medial femoral condyle 316, lateral femoral condyle 318, patellar sulcus320, and the size and geometry of features of the proximal tibia (e.g.medial and lateral plateau, medial and lateral eminence peaks, tubercle,lateral and medial epicondyle, popliteal sulcus, and so on) can also bemeasured and located using the sensor 310 and that data, along with theassociated soft tissue location data can be stored for access. As willbe discussed subsequently, the data can be used to generate algorithmsthat are adapted to predict a location and/or shape of a patient's softtissue based upon medical images of the patient's joint takenpreoperatively. According to some examples, data regarding bone size andbone features can be cross-referenced to associated soft tissue size,location, and shape. Algorithms can be generated that can utilize bonesize and/or bone features as ascertained by medical images to aid in theprediction of associated soft tissue size, location, and shape.

FIG. 4 shows an image of a proximal portion of a tibia 350. According toFIG. 4, the image can be generated from image data collected usingimaging technology and can be overlaid with data collected duringvarious tests using the sensor 310 (FIG. 3). As shown in FIG. 4, thecollected data can include a location of the ACL and PCL on the proximaltibia and can include a location of the lateral and medial intercondyleeminences.

FIG. 5 is a flowchart illustrating a method for determining a patientspecific soft tissue location according to an example of the presentdisclosure. The method can create 410 an average ACL and/or PCL contourfor an average femur and/or tibia. The contour can include one or moreof attachment location, size, and shape of the ACL and/or PCL. Thecreation of the average ACL and/or PCL contour can be derived from thedatabase of femoral and tibial data with identified (known) ACL and/orPCL attachment location information as discussed previously. The methodcan image 412 the patient's knee and measure 414 the morphology of oneor more of the bones (including size, shape, curvature, bone features,etc.) from the image. The data can be derived from medical images usingthe imaging technologies as described previously. According to theexample of FIG. 5, the method changes 416 (e.g., fits) the appropriatefemur and/or tibia model in the database of step 410 to match that ofthe patient's femur and/or tibia. According to some examples, differentmodels can be utilized for different sizes, genders, ethnicities, and soon. For example, this step can change the bone size and/or bone featuresof the selected knee model in the database to match the bone size and/orbone features of the patient's knee. In some examples, the method canmorph geometry as desired, for example by performing a lineartransformation. An example of such transformation can include, breakingthe object (e.g., bone) into several two dimensional images, taking oneof the two dimensional images and comparing it to another of the twodimensional images from the database, making both the images have thesame number of points, moving the points in the second two dimensionalimage from the database to be equal to that of the initial twodimensional image from the patient, performing the prior activity withseveral of the two dimensional images, creating a mathematicaltransformation that describes the differences between the two shapes inthree dimensions, and transforming any other information (e.g., bonyinformation, soft tissue attachment site) from the database to thespecific patient image(s).

Thus, the method can change 418 the PCL and/or ACL contour with thechange in the average femur and/or tibia of step 416. According to theillustrated method, the attachment location (and/or other ACL and/or PCLinformation) can be ascertained 420 and such information can be utilizedby the user for preoperative planning.

FIG. 6 is a flowchart of a method for among other things preoperativeplanning, selecting a prosthesis, and/or selecting or fabricating a jigbased on the soft tissue location and other patient morphology,according to an example of the present disclosure. The jig can be forarthroplasty or for perform ACL and PCL reconstruction as desired.According to the method of FIG. 6, the patient's knee can be imaged 510and the morphology of the knee can be measured 512 as previouslydescribed. The ACL and/or PCL of the patient can be located 514 relativeto the tibia and/or femur. The surgeon can preoperatively plan thepatient's surgery 516 using the location of the ACL and/or PCL and otherpatient morphology (e.g., bone size, bone features). According to oneexample, preoperative planning can include providing instructions,visual aid, information, recommendation, and automated measurement tothe surgeon. An example of preoperative planning software having suchfunctionality is illustrated and further discussed in reference to FIG.9. Examples of software, modules, and techniques that can be utilizedwith those disclosed herein are disclosed in United States PatentApplication Publication 2015/0066150 A1 owned by the Applicant, andincorporated herein by reference in its entirety.

According to another example, the preoperative planning can visuallydisplay the location of the ACL and/or PCL as illustrated in FIGS. 7 and9 and can allow the surgeon to virtually place tibial and/or femoralimplants. According to further examples, the preoperative planning canvisually display the location of the ACL and/or PCL for femoral jigsizing and placement as illustrated in FIG. 8. According to furtherexamples, the method can create 520 or select a jig used to ensureaccurate position and orientation of finishing instruments during boneresection as shown in FIG. 8. Examples of creation of a jig aredescribed in U.S. Pat. No. 8,884,618 and United States PatentApplication Publication 2013/0317510 A1 owned by the Applicant, andincorporated herein by reference in their entirety. In FIG. 6, themethod can allow the user to select 522 appropriate tibial and/orfemoral implants based upon the location of the ACL and/or PCL and otherpatient morphology (e.g., bone size, bone features). As illustrated inFIG. 6, virtual placement of the tibial and/or femoral implants, virtualdisplay of the ACL and/or PCL, selection of implant(s), and creation ofthe jig can be interrelated or can be performed independent orsemi-independent of one another.

FIG. 7 uses a virtual representations of the location of a patient'ssoft tissues 610 relative to a tibia 612, a femur 614, and a prosthesis616, according to an example of the present disclosure. Such virtualrepresentations can aid the surgeon in preoperative planning. Forexample, the surgeon can alter the size or brand of the implant and seethe effects on the arthroplasty including any effects on the softtissues 610. According to another example, the surgeon can change otheraspects such as the location of one or more resections utilized for theknee arthroplasty and see the effects on the arthroplasty including anyeffects on the soft tissues 610. FIG. 8 uses a virtual representationsof the location of a patient's soft tissues 710 for sizing and locatinga jig 712 on a femur 714, according to an example of the presentdisclosure. Such virtual representations can aid the surgeon inpreoperative planning.

FIG. 9 is a screen shot image of a preoperative planning tool 910including a visual display generated for a surgeon for preoperativeplanning, according to an example of the present disclosure. Asdiscussed, the planning tool 910 can include various functions such asproviding instructions, visual aid, information, recommendation, andautomated measurement to the surgeon.

In the example of FIG. 9, the planning tool 910 can allow the surgeon tovisualize and alter resections (indicated by yellow lines) and can allowthe surgeon to virtually install the implant(s) for review. The planningtool 910 can estimate a size of a femoral implant and tibial implant anddisplay such size to the surgeon. The planning too 910 can also allowfor the virtual selection and display of various implants according tosize and/or brand. Furthermore, the planning tool 910 can virtuallydisplay the soft tissues of the patient according to the estimatedlocation or actual as discussed herein.

Certain examples are described herein as including logic or a number ofcomponents, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied on a machine-readable medium or ina transmission signal) or modules. A module is tangible unit capable ofperforming certain operations and may be configured or arranged in acertain manner. In examples, one or more computer systems (e.g., astandalone, client or server computer system) or one or more modules ofa computer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa module that operates to perform certain operations as describedherein.

In various examples, a module may be implemented mechanically orelectronically. For example, a module may comprise dedicated circuitryor logic that is permanently configured (e.g., as a special-purposeprocessor, such as a field programmable gate array (FPGA) or anapplication-specific integrated circuit (ASIC)) to perform certainoperations. A module may also comprise programmable logic or circuitry(e.g., as encompassed within a general-purpose processor or otherprogrammable processor) that is temporarily configured by software toperform certain operations. It will be appreciated that the decision toimplement a module mechanically, in dedicated and permanently configuredcircuitry, or in temporarily configured circuitry (e.g., configured bysoftware) may be driven by cost and time considerations.

Accordingly, the term “module” can be understood to encompass a tangibleentity, such as hardware, that can be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired) or temporarilyconfigured (e.g., programmed) to operate in a certain manner and/or toperform certain operations described herein. Considering examples inwhich modules are temporarily configured (e.g., programmed), each of themodules need not be configured or instantiated at any one instance intime. For example, where the modules comprise a general-purposeprocessor configured using software, the general-purpose processor maybe configured as respective different modules at different times.Software may accordingly configure a processor, for example, toconstitute a particular module at one instance of time and to constitutea different module at a different instance of time.

Modules can provide information to, and receive information from, othermodules. Accordingly, the described modules may be regarded as beingcommunicatively coupled. Where multiple of such modules existcontemporaneously, communications may be achieved through signaltransmission (e.g., over appropriate circuits and buses) that connectthe modules. In examples in which multiple modules are configured orinstantiated at different times, communications between such modules maybe achieved, for example, through the storage and retrieval ofinformation in memory structures to which the multiple modules haveaccess. For example, one module may perform an operation, and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further module may then, at a later time,access the memory device to retrieve and process the stored output.Modules may also initiate communications with input or output devices,and can operate on a resource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some examples, comprise processor-implementedmodules.

Similarly, the methods described herein may be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod may be performed by one or more processors orprocessor-implemented modules. The performance of certain of theoperations may be distributed among the one or more processors, not onlyresiding within a single machine, but deployed across a number ofmachines. In some example examples, the processor or processors may belocated in a single location (e.g., within a home environment, an officeenvironment or as a server farm), while in other examples the processorsmay be distributed across a number of locations.

The one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., Application Program Interfaces (APIs).)

Examples may be implemented in digital electronic circuitry, or incomputer hardware, firmware, software, or in combinations of them.Examples may be implemented using a computer program product, e.g., acomputer program tangibly embodied in an information carrier, e.g., in amachine-readable medium for execution by, or to control the operationof, data processing apparatus, e.g., a programmable processor, acomputer, or multiple computers.

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a module, subroutine,or other unit suitable for use in a computing environment. A computerprogram can be deployed to be executed on one computer or on multiplecomputers at one site or distributed across multiple sites andinterconnected by a communication network.

In examples, operations may be performed by one or more programmableprocessors executing a computer program to perform functions byoperating on input data and generating output. Method operations canalso be performed by, and apparatus of examples may be implemented as,special purpose logic circuitry, e.g., a field programmable gate array(FPGA) or an application-specific integrated circuit (ASIC).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inexamples deploying a programmable computing system, it will beappreciated that both hardware and software architectures requireconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor), or a combinationof permanently and temporarily configured hardware may be a designchoice. Below are set out hardware (e.g., machine) and softwarearchitectures that may be deployed, in various examples.

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments in which theinvention can be practiced. These embodiments are also referred toherein as “examples.” Such examples can include elements in addition tothose shown or described. However, the present inventors alsocontemplate examples in which only those elements shown or described areprovided. Moreover, the present inventors also contemplate examplesusing any combination or permutation of those elements shown ordescribed (or one or more aspects thereof), either with respect to aparticular example (or one or more aspects thereof), or with respect toother examples (or one or more aspects thereof) shown or describedherein.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In this document, the terms “including” and “inwhich” are used as the plain-English equivalents of the respective terms“comprising” and “wherein.” Also, in the following claims, the terms“including” and “comprising” are open-ended, that is, a system, device,article, composition, formulation, or process that includes elements inaddition to those listed after such a term in a claim are still deemedto fall within the scope of that claim. Moreover, in the followingclaims, the terms “first,” “second,” and “third,” etc. are used merelyas labels, and are not intended to impose numerical requirements ontheir objects.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Otherexamples can be used, such as by one of ordinary skill in the art uponreviewing the above description. The Abstract is provided to comply with37 C.F.R. § 1.72(b), to allow the reader to quickly ascertain the natureof the technical disclosure. It is submitted with the understanding thatit will not be used to interpret or limit the scope or meaning of theclaims. Also, in the above detailed description, various features may begrouped together to streamline the disclosure. This should not beinterpreted as intending that an unclaimed disclosed feature isessential to any claim. Rather, inventive subject matter may lie in lessthan all features of a particular disclosed example. Thus, the followingclaims are hereby incorporated into the detailed description as examplesor embodiments, with each claim standing on its own as a separateexample, and it is contemplated that such examples can be combined witheach other in various combinations or permutations. The scope of theinvention should be determined with reference to the appended claims,along with the full scope of equivalents to which such claims areentitled.

1.-20. (canceled)
 21. A method comprising: creating an average of one ormore of an ACL or PCL contour for one or more of an average femur ortibia from a soft tissue data and a bone data corresponding to a targetlocation of the orthopedic implant, wherein the creating the average isderived from a database of femoral and tibial data with identified knownone or more of ACL or PCL attachment location information; for aspecific patient, imaging a target location corresponding to the targetlocation of the orthopedic implant to collect image data regarding amorphology of the specific patient, the morphology including at leastone of a bone size or a bone feature; measuring the morphology resultingfrom the imaging; altering one or more of the average femur or tibia tomatch one or more of a femur or tibia of the specific patient; andaltering one or more of an ACL or PCL contour of the specific patientwith the step of altering one or more of the average femur or tibia. 22.The method of claim 21, wherein the altering the one or more of the ACLor PCL contour of the specific patient is a linear transformation withrespect to the altering one or more of the average femur or tibia. 23.The method claim 21, wherein the altering the one or more of the ACL orPCL contour of the specific patient includes breaking the image datainto several two dimensional images, taking one of the two dimensionalimages and comparing the one of the two dimensional images to another ofthe two dimensional images from the database.
 24. The method of claim23, further comprising making both the one of the two dimensional imagesand the another of the two dimensional images from the database have asame number of reference points.
 25. The method of claim 24, whereinmaking both the one of the two dimensional images and the another of thetwo dimensional images from the database have a same number of referencepoints comprises moving the reference points in the another twodimensional image from the database to be equal to that of the one ofthe two dimensional images.
 26. The method of claim 25, furthercomprising performing the making of claim 24 with the several of the twodimensional images.
 27. The method of claim 21, wherein the altering theone or more of the ACL or PCL contour of the specific patient includescreating a mathematical transformation that describes the differencesbetween the one or more of the ACL or PCL contour of the specificpatient and the average of one or more of an ACL or PCL contour in threedimensions, and transferring other information from the database offemoral and tibial data to the image data of the specific patient. 28.The method of claim 21, further comprising displaying data including alocation of the one or more of the ACL or PCL contour of the specificpatient.
 29. The method of claim 28, comprising constructing, based atleast in part on the location of the one or more of the ACL or PCLcontour of the specific patient, a virtual model of the target locationof the specific patient that displays the one or more of the ACL or PCLcontour.
 30. The method of claim 21, further comprising recommending aprosthesis to best fit one or more of a femur or tibia of the specificpatient.
 31. The method of claim 21, wherein the morphology furtherincludes one of a soft tissue shape and a soft tissue location.
 32. Asystem comprising: a computer including at least one processor and amemory device, the memory device including data regarding an average ofone or more of an ACL; or PCL contour for one or more of an averagefemur or tibia derived from a soft tissue data and a bone datacorresponding to a target location of the orthopedic implant, the memoryincluding instructions that, when executed by the at least oneprocessor, cause the computer to: collect image data regarding amorphology of the specific patient, the morphology including at leastone of a bone size and a bone feature for a specific patient and isderived from imaging a target location corresponding to the targetlocation of the orthopedic implant; measure the morphology; alter one ormore of the average femur or tibia to match one or more of a femur ortibia of the specific patient; and alter one or more of an ACL or PCLcontour of the specific patient with the step of altering one or more ofthe average femur or tibia.
 33. The system of claim 32, wherein theaverage of one or more of the ACL or PCL contour is derived from adatabase of femoral and tibial data with identified known one or more ofACL or PCL attachment location information.
 34. The system of claim 32,further comprising instructions that, when executed by the at least oneprocessor, cause the computer to construct, based at least in part onthe location of the one or more of the ACL or PCL contour of thespecific patient, a virtual model of the target location of the specificpatient that displays the one or more of the ACL or PCL contour.
 35. Thesystem of claim 32, further comprising instructions that, when executedby the at least one processor, cause the computer to recommend aprosthesis to best fit one or more of a femur or tibia of the specificpatient.
 36. The system of claim 32, wherein alteration of the one ormore of the ACL or PCL contour of the specific patient is a lineartransformation with respect to alteration one or more of the averagefemur or tibia.
 37. The system claim 32, wherein alteration of the oneor more of the ACL or PCL contour of the specific patient includesbreaking the image data into several two dimensional images, taking oneof the two dimensional images and comparing the one of the twodimensional images to another of the two dimensional images from thedatabase.
 38. The system of claim 37, further comprising making both theone of the two dimensional images and the another of the two dimensionalimages from the database have a same number of reference points.
 39. Thesystem of claim 38, wherein making both the one of the two dimensionalimages and the another of the two dimensional images from the databasehave a same number of reference points comprises moving the referencepoints in the another two dimensional image from the database to beequal to that of the one of the two dimensional images.
 40. The systemof claim 32, wherein the altering one or more of the ACL or PCL contourof the specific patient includes creating a mathematical transformationthat describes the differences between the one or more of the ACL or PCLcontour of the specific patient and the average of one or more of an ACLor PCL contour in three dimensions, and transferring other informationfrom the database of femoral and tibial data to the image data of thespecific patient.